Safety-relevant vehicle assistance systems need to be capable of predicting dangerous situations which can result in a loss of vehicle control or in a collision. If such a situation is predicted in good time, it can sometimes be avoided, specifically autonomously, for example by slowing down the vehicle, or by warning the driver about this imminent situation using a warning system.
This forecast capability has previously been provided by various types of sensors, radar systems or cameras, but these are all limited to the direct field of vision of the vehicle. This means that they are not able to provide any information about what happens after a bend, for example. Map data can be used to improve these predictions and, by way of example, to be able to forecast how the road proceeds after the next bend. In order to use the information on a map, it is necessary to know the current position. This current position can be identified using various means, e.g. with a GPS receiver, which can be an inexpensive and global solution.
The GPS or another positioning system has an error which does not always allow a position to be indicated accurately on a road. Furthermore, a map may also always have some accuracy errors and discrepancies from the actual cartography, which makes the position-finding directly on a road more difficult or even impossible.